Model-based Control System Design Research Articles

Design of a Hierarchical-Type Control System Based on Smart MBD Approach and its Application to Hydraulic Excavator

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm.

Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to predict ankle joint torque induced by electrical stimulation, which used variable forgetting factor recursive least squares (VFFRLS) method to update the model parameters. To validate the proposed model, ten healthy individuals were recruited for short-duration FES experiments, ten for long-duration FES experiments, and three stroke patients for both. The isometric ankle dorsiflexion torque induced by FES was measured, and then the test performance of the fixed-parameter Hammerstein model, the adaptive Hammerstein model based on fixed forgetting factor recursive least squares (FFFRLS) and the adaptive Hammerstein model based on VFFRLS was compared. The goodness of fit, root mean square error, peak error and success rate were applied to evaluate the accuracy and stability of the model. The results indicate a significant improvement in both the accuracy and stability of the proposed adaptive model compared to the fixed-parameter model and the adaptive model based on FFFRLS. The proposed adaptive model enhances the ability of the model to cope with muscle changes.

Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing.

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.

Modelling the Wind Turbine by Using the Tip-Speed Ratio for Estimation and Control

The development of dynamic models for control purposes is characterised by the challenge of finding a compromise between the minimum necessary information about the system dynamics contained in the model and a model with a low level of complexity such that the model-based control system design becomes comfortable. To achieve this balance, a modified dynamic model for the drivetrain of a wind turbine is proposed in this contribution. The main idea is to introduce the tip-speed ratio as a state variable so that an interval observer can be designed in such a way that its estimates can be used in the torque control during the partial load operation as well as for the estimation of the effective wind speed. During the runtime, the observer’s matrix gain is recalculated to adapt the behaviour to the current operational state, which changes all the time with the wind speed. Besides the theoretical formulation, a numerical example of a 20 MW reference wind turbine illustrates the utility of the method. The results show good control performance concerning the tip-speed ratio control loop and a satisfactory estimation of the effective wind speed.

Lyapunov-based economic model predictive control for online model discrimination

Economic model predictive control (EMPC) is a flexible control design strategy that can be modified to achieve many operating goals while also ensuring safe operation (e.g., by adding Lyapunov-based stability constraints to form Lyapunov-based EMPC, or LEMPC). Prior works have investigated LEMPC capabilities for achieving goals online beyond optimizing process economics, including aiding in model structure selection to benefit model-based control system design since the accuracy and quality of the process model are important for achieving an expected performance from such systems. This work further probes the capabilities of LEMPC to accomplish multiple objectives during process operation, including aiding in the discrimination between mechanistic models online. In particular, several rival mechanistic models may explain the existing data. To discard models from this set that do not fully represent the actual process, a new set of “online experiments” can be conducted to collect more information. However, additional experimentation may be costly and unsafe to be performed. LEMPC can aid in performing online data collection when discrimination between mechanistic models is needed, with the flexibility to ensure safety as the data is gathered and trade off the data-gathering goal for cost considerations. Motivated by this, we discuss how LEMPC can be designed to automatically and dynamically collect data that is useful for the selection of mechanistic models from among a set of possibilities. A chemical process example is used to clarify benefits and limitations of LEMPC for promoting online model discrimination.

Investigation of VVER-1200 pressurizer dynamics by adopting modelica based modeling

This work is conducted within the framework of developing a control-oriented modelling and simulation tool focused on the study of the generation III + VVER-1200 plant transient dynamics, in order to conduct necessary stability analyses aimed at providing essential feedbacks required to perform the model-based control system design. An object-oriented modelling technique was applied, based on the reliable, validated and well-documented Modelica language. The pressurizer was modeled based on the Åström and Bell non-equilibrium approach. The developed model was then adapted to predict the behavior of the VVER-1200 pressurizer free dynamics at different conditions, with different perturbations/excursions. The developed model has been successfully integrated inside a Modelica based VVER-1200 plant model and pressurizer transient response to plant different free dynamics was tested.Test and verification of the developed model were performed against multiple benchmarks, as well as a real reference plant. Good agreement was obtained. Test results has shown that the simulator has provided precise information regarding its steady state as well as transient behavior even for severe excursions. Uncertainty analysis was also performed and showed that the developed model is more sensitive to the input variables than the plant model parameters. The model is valid along the complete operating range, including run-up/down, fast runbacks, as well as violent transients. The produced model is non-linear, time variant, of low order and high-precision. The adopted technique is well suited to the task of performing assessed studies for system dynamics. It fills the gap and overcomes shortages and/or limitations associated with current control-oriented modeling techniques.

Data-Driven Multiobjective Controller Optimization for a Magnetically Levitated Nanopositioning System

The performance achieved with traditional model-based control system design approaches typically relies heavily on accurate modeling of the motion dynamics. However, modeling the true dynamics of present-day increasingly complex systems can be an extremely challenging task; and the usually necessary practical approximations often renders the automation system to operate in a nonoptimal condition. This problem can be greatly aggravated in the case of a multiaxis magnetically levitated (maglev) nanopositioning system where the fully floating behavior and multiaxis coupling make extremely accurate identification of the motion dynamics largely impossible. On the other hand, in many related industrial automation applications, e.g., the scanning process with the maglev system, repetitive motions are involved which could generate a large amount of motion data under nonoptimal conditions. These motion data essentially contain rich information; therefore, the possibility exists to develop an intelligent automation system to learn from these motion data, and to drive the system to operate toward optimality in a data-driven manner. Along this line then, this article proposes a data-driven model-free controller optimization approach that learns from the past nonoptimal motion data to iteratively improve the motion control performance. Specifically, a novel data-driven multiobjective optimization approach is proposed that is able to automatically estimate the gradient and Hessian purely based on the measured motion data; the multiobjective cost function is suitably designed to take into account both smooth and accurate trajectory tracking. In this article, experiments are then conducted on the maglev nanopositioning system to demonstrate the effectiveness of the proposed method, and the results show rather clearly the practical appeal of our methodology for related complex robotic systems with no accurate model available.

Structured Control Design for a Highly Flexible Flutter Demonstrator

The model-based flight control system design for a highly flexible flutter demonstrator, developed in the European FLEXOP project, is presented. The flight control system includes a baseline controller to operate the aircraft fully autonomously and a flutter suppression controller to stabilize the unstable aeroelastic modes and extend the aircraft’s operational range. The baseline control system features a classical cascade flight control structure with scheduled control loops to augment the lateral and longitudinal axis of the aircraft. The flutter suppression controller uses an advanced blending technique to blend the flutter relevant sensor and actuator signals. These blends decouple the unstable modes and individually control them by scheduled single loop controllers. For the tuning of the free parameters in the defined controller structures, a model-based approach solving multi-objective, non-linear optimization problems is used. The developed control system, including baseline and flutter control algorithms, is verified in an extensive simulation campaign using a high fidelity simulator. The simulator is embedded in MATLAB and a features non-linear model of the aircraft dynamics itself and detailed sensor and actuator descriptions.

Control-oriented dynamic model of an inductively coupled plasma torch by artificial intelligence methodology

Inductively coupled plasma (ICP) torches have been widely used in various materials processing. To improve the control of their processes, there has been a growing demand for simplified numerical models which can rapidly predict the dynamics of plasma jets subject to time-varying inputs or external disturbances. In this paper, control-oriented dynamic models of an ICP torch are developed as an alternative to the complex, high-level 2D time-dependent numerical model (i.e. a model based on magneto-hydrodynamic equations). Prior to model development, the detailed dynamic and nonlinear nature of the ICP torch to its time-varying operation conditions was numerically investigated using a 2D numerical model to gain insight into choosing appropriate dynamic model structures. Linear ARX (AutoRegressive with eXogenous input) and ARX-type neural network models were selected in the model identification, and their parameters or weightings were determined using the input/output data obtained from the 2D time-dependent numerical model. The dynamic behaviours of the ICP torch predicted from the developed models were in good agreement with the data from the 2D time-dependent numerical model. Using the developed models, a simple control system for the plasma temperature and axial velocity regulations was also designed and tested. The feedback control simulations demonstrated good set point tracking and disturbance rejection performances, indicating that the developed approach can be directly applied to the model-based control system design of an ICP torch as well as other thermal plasma torches.

Design, development, and control of a tough electrohydraulic hexapod robot for subsea operations

In this paper, the design, the development, and the control for an 18 degree-of-freedom electrohydraulic hexapod robot for subsea operations are presented. The hexapod, called HexaTerra, can be equipped with a trenching machine, and move over obstacles and on sloped terrain. Optimization techniques are employed to size the robot legs. Rigid body equations of motion and hydraulic dynamics are developed. Compact electrohydraulic components are sized and selected taking into account the leg kinematics and system dynamic analysis. A model-based control system design is implemented in a real-time environment, able to produce the overall functionality and performance. Experimental results obtained from preliminary tests with the developed electrohydraulic hexapod show good controlled performance and demonstrate excellent system stability over obstacles.

Dipole and multipole models of dielectrophoresis for a non‐negligible particle size: Simulations and experiments

Mathematical models of dielectrophoresis play an important role in the design of experiments, analysis of results, and even operation of some devices. In this paper, we test the accuracy of existing models in both simulations and laboratory experiments. We test the accuracy of the most common model that involves a point-dipole approximation of the induced field, when the small-particle assumption is broken. In simulations, comparisons against a model based on the Maxwell stress tensor show that even the point-dipole approximation provides good results for a large particle close to the electrodes. In addition, we study a refinement of the model offered by multipole approximations (quadrupole, and octupole). We also show that the voltages on the electrodes influence the error of the model because they affect the positions of the field nulls and the nulls of the higher-order derivatives. Experiments with a parallel electrode array and a polystyrene microbead reveal that the models predict the force with an error that cannot be eliminated even with the most accurate model. Nonetheless, it is acceptable for some purposes such as a model-based control system design.

Research of Model-based Aeroengine Control System Design Structure and Workflow

The control system of aeroengine design is a complex system engineeringincludingmany procedure of different subsystems. To improve the efficiency of areoengine control system design and reduce the cost and development period of research procedure, model-based areoengine control system design method can be used for integrating systems, collaborative design and optimizing design. Model-based aeroengine control system design workflow consists of the phases from analysis of system requirements to semi-physics simulation verification. The management approach and design procedure can help to separate the whole design workflow into different sub-phases which can help to scheme the concurrent design procedure of complex control system in order to improve the

Enhanced Dynamic Model of Pneumatic Muscle Actuator with Elman Neural Network

To make effective use of model-based control system design techniques, one needs a good model which captures system’s dynamic properties in the range of interest. Here an analytical model of pneumatic muscle actuator with two pneumatic artificial muscles driving a rotational joint is developed. Use of analytical model makes it possible to retain the physical interpretation of the model and the model is validated using open-loop responses. Since it was considered important to design a robust controller based on this model, the effect of changed moment of inertia (as a representation of uncertain parameter) was taken into account and compared with nominal case. To improve the accuracy of the model, these effects are treated as a disturbance modeled using the recurrent (Elman) neural network. Recurrent neural network was preferred over feedforward type due to its better long-term prediction capabilities well suited for simulation use of the model. The results confirm that this method improves the model performance (tested for five of the measured variables: joint angle, muscle pressures, and muscle forces) while retaining its physical interpretation.

An enhanced physics-based model to estimate the displacement of piezoelectric actuators

Piezoelectric actuators are the foremost actuators in the area of nanopositioning. However, the sensors employed to measure the actuator displacement are expensive and difficult, if not impossible, to use. Mathematical models can map the easy-to-measure electrical signals to the displacements of the actuators as the displacement sensors are replaced with the models. In addition, these models can be used in model-based control system design. Two main groups of mathematical models are used for this purpose: black box and physics-based models. As an advantage, the latter has a much smaller number of parameters reducing computational demand in real-time applications. However, physics-based models suffer from (1) the relatively low accuracy of the models and (2) non-standard and ad-hoc parameter identification methods. In this research, to improve the model accuracy, mathematical structure of a well-known physics-based model, the Voigt model, is enhanced by adding two complementary terms inspired by another model, the Preisach model. Then, a standard method based on the evolutionary algorithms is proposed to identify the model’s parameters. The proposed ideas are substantiated to increase the applicability and accuracy of the model, and they are easily extendable to other physics-based models of piezoelectric actuators. The newly proposed enhanced structure of the Voigt model doubles the estimation accuracy of the original model and results in accuracies comparable with black box models.

The Butterfly-Shaped Feedback Loop in Networked Control Systems for the Unknown Delay Compensation

Available model-based networked control system (NCS) design approaches are basically formulated from the known system model and the information of the networked-induced delay time. Because the delay in remote control systems is significant and time-varied as commercial networks involved, available model- based NCS design approaches may thus become impractical. The increase of time delay due to the limited communication bandwidth usually induces data dropout to make NCS design even more difficult.Inthispaper,theschemeoftheperfectdelaycompensation (PDC) is proposed by introducing the butterfly-shaped inner feed- back loop in NCS to deal with the unknown network-induced time delay. Furthermore, analytical results indicate that the controller obtained in general control systems can be directly implemented on NCS with the proposed PDC scheme. Both simulation and experi- mental results were successfully carried out on an ac servo motor at a distance of 15 km through Ethernet to maintain a stable remote control system.

Object-oriented modelling and simulation for the ALFRED dynamics

In this paper, a control-oriented modelling and simulation tool for the study of the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED) plant dynamics is presented. It has been developed in order to perform design-basis transient analyses aimed at providing essential feedbacks for the system design finalization. The simulator has been meant to be modular, open and efficient. In this perspective, an object-oriented modelling approach has been adopted, by employing the reliable, tested and well-documented Modelica language. Simulation of core behaviour is based on point kinetics for neutronics and one-dimensional heat transfer models for thermal-hydraulics, coherently with ALFRED specifications. An effort has been spent to model the bayonet-tube Steam Generator (SG) foreseen to be installed within the reactor vessel. The primary loop model has been built by connecting the above-mentioned components (taking into account suitable time delays) and by incorporating the cold pool, which has revealed to be fundamental for an accurate definition of the time constants characteristic of the system because of its large thermal inertia. The description of the overall plant has been finalized by connecting standard turbine, condenser and other components of the balance of plant. Afterwards, the reactor responses to three typical transient initiators have been simulated (i.e., reduction of feedwater mass flow rate, variation of the turbine admission valve coefficient and transient of overpower). Simulation outcomes confirm the strong coupling between core and SG, besides showing the characteristic time constants of the various component responses. Results of the present study constitute a starting point in the definition of plant control strategies, laying the basis for investigation and development of a model-based control-system design.

Hybrid dynamic modeling for two phase flow condensers

In this paper, a hybrid modeling approach is proposed to describe the dynamic behavior of the two phase flow condensers used in air-conditioning and refrigeration systems. The model is formulated based on fundamental energy and mass balance governing equations, and thermodynamic principles, while some constants and less important variables that change very little during normal operation, such as cross-sectional areas, mean void fraction, the derivative of the saturation enthalpy with respect to pressure, etc., are lumped into several unknown parameters. These parameters are then obtained by experimental data using least squares identification method. The proposed modeling method takes advantages of both physical and empirical modeling approaches, can accurately predict the transient behaviors in real-time and significantly reduce the computational burden. Other merits of the proposed approach are that the order of the model is very low and all the state variables can be easily measured. These advantages make it easy to be applied to model based control system design. The model validation studies on an experimental system show that the model predicts the system dynamic well.

Introducing uncertainty analysis of nucleation and crystal growth models in Process Analytical Technology (PAT) system design of crystallization processes

This paper presents the application of uncertainty and sensitivity analysis as part of a systematic model-based process monitoring and control (PAT) system design framework for crystallization processes. For the uncertainty analysis, the Monte Carlo procedure is used to propagate input uncertainty, while for sensitivity analysis, global methods including the standardized regression coefficients (SRC) and Morris screening are used to identify the most significant parameters. The potassium dihydrogen phosphate (KDP) crystallization process is used as a case study, both in open-loop and closed-loop operation. In the uncertainty analysis, the impact on the predicted output of uncertain parameters related to the nucleation and the crystal growth model has been investigated for both a one- and two-dimensional crystal size distribution (CSD). The open-loop results show that the input uncertainties lead to significant uncertainties on the CSD, with appearance of a secondary peak due to secondary nucleation for both cases. The sensitivity analysis indicated that the most important parameters affecting the CSDs are nucleation order and growth order constants. In the proposed PAT system design (closed-loop), the target CSD variability was successfully reduced compared to the open-loop case, also when considering uncertainty in nucleation and crystal growth model parameters. The latter forms a strong indication of the robustness of the proposed PAT system design in achieving the target CSD and encourages its transfer to full-scale implementation.

Model-based control system design in a urea-SCR aftertreatment system based on NH3 sensor feedback

This paper presents preliminary control system simulation results in a urea-selective catalytic reduction (SCR) aftertreatment system based on NH3 sensor feedback. A four-state control-oriented lumped parameter model is used to analyze the controllability and observability properties of the urea-SCR plant. A model-based estimator is designed via simulation and a control system is developed with design based on a sliding mode control framework. The control system based on NH3 sensor feedback is analyzed via simulation by comparing it to a control system developed based on NOx sensor feedback. Simulation results show that the NH3 sensor-based strategy performs very similarly in comparison to a NOx sensor-based strategy. The control system performance metrics for NOx index, urea index, urea usage, and NH3 slip suggest that the NOx sensor can be a potential alternative to a NOx sensor for urea-SCR control applications.

Modelling thermal processes in buildings using an object-oriented approach and Modelica

Most of today’s modelling and simulation concepts originate from the times and methods of analog computers. Usually, it is assumed that the model must be expressed in an explicit state-space form. Consequently, the topology of the system gets lost and any future extension and reuse of the model is tedious and error-prone. In other words, it is the modeller’s task to consider the computational order of the operations during a simulation. In this paper we discuss the re-implementation of a passive-solar- building simulator in an object-oriented environment; it was originally built in the non-object-oriented simulation environment of Matlab–Simulink. The former simulator was designed to resemble a real physical test chamber with regard to the thermal and solar radiation flows. However, due to the lack of object orientation in Matlab–Simulink it was very difficult to apply any configuration modifications and extensions. We start with a brief description of the mathematical modelling which includes thermal dynamics and solar radiation. Then the implementation in Modelica is presented. So, a much superior environment in comparison with Matlab-Simulink was obtained, giving us the possibility of high-level modular and object-oriented modelling. The model is also extremely efficient in multidisciplinary projects in which control-engineering specialists (our group) cooperate with specialists from civil engineering, because civil engineers can more easily understand graphical and textual models in Modelica than schemes in Simulink. We expect that such a model will fulfil and significantly improve several model properties in comparison to the Matlab–Simulink implementation, i.e., a better understanding of the influences of thermal and radiation flows on comfortable living conditions, a model-based control-system design, which will enable the harmonization of active and passive energy resources, important energy savings, and a very suitable environment for education in modelling, simulation and control.